Biohub: The Future of Biology is Open-Source with Co-Founders Mark Zuckerberg, Priscilla Chan, and Head of Science Alex Rives

Priscilla Chan: So Biohub in its current form, we're super excited about. We feel like it's a really good fit for who we are and what we bring to the table and what we can achieve together. But this work started 10 years ago when we were thinking about how can we give back. And Mark wanted to build an organization that could cure, prevent, and manage all disease by the end of the century. And we had a series of hilarious meetings with scientists that like famous Nobel Prize winning scientists were just laughing at us. ... Our goal was always to build tools that could accelerate the whole scientific fields. That way the scientific field collectively could cure all the diseases. But still, people thought that by the end of the century was a stretch. Now I think it's like too conservative.

Priscilla Chan: Finally, we got people to like, they're like, fine, if you really must know. And we're like, you know, we do. It seems important. It's, you know, they were like, well, we work in silos. And when you publish, information doesn't get shared. It gets locked up for long periods of time. And we don't have tooling. they gave the example of like, we build a great tool by 1 postdoc in a lab and it lives on their computer. And when they graduate, the tool is gone. And they just, it was, what we heard was very hard to build shared tools to move science faster. ... Build a shared knowledge base to quickly move science faster. And that's sort of where we begin in thinking about, okay, like if those are the problems, like what can we contribute?

Priscilla Chan: Finally, we got people to like, they're like, fine, if you really must know. And we're like, you know, we do. It seems important. It's, you know, they were like, well, we work in silos. And when you publish, information doesn't get shared. It gets locked up for long periods of time. And we don't have tooling. they gave the example of like, we build a great tool by 1 postdoc in a lab and it lives on their computer. And when they graduate, the tool is gone. And they just, it was, what we heard was very hard to build shared tools to move science faster. build a shared knowledge base to quickly move science faster. And that's sort of where we begin in thinking about, okay, like if those are the problems, like what can we contribute?

Mark Zuckerberg: I mean, I think each layer is going to end up being somewhat qualitatively different, right? I mean, the, but you need to be able to understand the protein interactions in order to be able to understand how cells work. So you can't just go straight to cells in a way without understanding the protein modeling. And then if you're trying to understand something like the, you know, the way the immune system works or a bunch of cells interact together, then it's tough to do that without first understanding cells. I mean, you might be able to, at a very high level of abstraction, simulate a system. But if you really want to understand how it's going to work, you kind of want to build the simulations at each level hierarchically. So that's basically the approach that we're going through, starting with the building blocks and the protein. But yeah, I mean, I think that there's going to be different types of data that you want to collect for each. The modeling techniques, I think we'll see. I mean, that'll all keep on advancing across the board. But I do think that a big part of the strategy is this view that you need to build it up hierarchically. And one of the things that's unique about us in the space is we were very intentional. that the AI efforts and the wet lab efforts were a single effort.

Alex Rives: So, as we think about mechanistic interpretability in those models, we're really seeing the unknown because the models have been trained on billions of protein sequences. They've been trained on, you know, both known and unknown biology. And yet they're developing these representations that start to kind of capture things that we can really see correspond to that reductive picture of biology that's been built up over the centuries. So kind of you can start to connect the dots between proteins where we kind of really don't know anything about them with proteins where we do know something because there's that kind of underlying structure grammar that's linking them in the representation space of the model. And at the extreme, it could be, we're going to understand systems in the body that we didn't before or the mechanism of action for a new treatment because we can ask the model, right, interrogate that representation.

Mark Zuckerberg: Well, I think we just want to give tools to the whole scientific community. ... I think in order to have the biggest impact, I mean, part of it is just we're, I mean, it's not actually clear that we couldn't run it as a business if we wanted to. I just think that we'll have a bigger impact by getting this in more scientists' hands quicker by doing it as open source projects instead. ... If you're building tools that are this complicated, you kind of want to have a 10 to 15 year time horizon on building out these efforts. ... the sort of neutral nonprofit nature of our work actually helps harness more people to enter this effort. And to actually achieve the mission of understanding the totality of human biology and to cure, prevent, manage all disease, you actually do need the entire academic biotech industry to come together ... There's a super long tail of diseases. There are the common ones, and even the common ones, I think if you unbundle heart disease, cancer, neurodegenerative diseases, even if you unbundle like dementia or depression, there are many, many, many subcategories that become more and more niche. And that's not even looking at the long tail of rare diseases. Those often get orphaned and don't get brought along when we're sort of looking at what the most efficient way to impact the lives of many. But if you sort of decentralize the effort and put the tools in many people's hands, you start getting people who are like, you know what? I am super interested in spinal muscular atrophy, and that's something I care deeply about. And if you put the tools in that person's hands, they're going to be able to make progress.

Priscilla Chan: The way I think about it is like, we want to understand how biology works. The ideal world is you would say, I understand the genetics of this person. So I want to think about people at the individual level. I want to understand the genetics of this person. I want to understand the risks they have to different illnesses. I want to understand the mechanistic connection between, say, a gene variant, a protein, and a disease process. Because if you understand that through chain, then you can design a protein, design a drug, bespoke to them, and actually make an intervention. And right now, I'm sure we've all had experiences being sick. And if you have something that's even remotely non-standard, you go into PubMed, you look up a paper, you look up the supplement, and then you start going through the methods, and you're like, am I represented in this paper? And we're just making guesses. We really have no mechanistic understanding. ... So my goal is to be able to treat the individual as an individual, understand the mechanisms, and be able to intervene.